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Paper

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

by Independent / Community 005a5ac1c06c1d6c4f7cb162a84d786c376133c1

Free2AITools Nexus Index

69.1

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 84

P: Popularity 60

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional inclusion vector...

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Semantic Scholar 53 Citations

Paper Information Summary
Entity Passport
Registry ID	005a5ac1c06c1d6c4f7cb162a84d786c376133c1
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{005a5ac1c06c1d6c4f7cb162a84d786c376133c1,
  author = {Unknown},
  title = {Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/005a5ac1c06c1d6c4f7cb162a84d786c376133c1}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection [Paper]. Free2AITools. https://api.semanticscholar.org/005a5ac1c06c1d6c4f7cb162a84d786c376133c1

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 84

Popularity (P) 60

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection: Authority (A:84), Popularity (P:60), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional inclusion vector..."

❝ Cite Node

@article{Unknown2026Distributional,
  title={Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈53CitationsSemantic Scholar

🏛️84AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

rag retrievalvector databases

📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 53

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